Specialized Chips

For 50 years, a foundational principle behind the development of microprocessors in computer chips has been Moore’s Law. This law is an observation made by Intel co-founder Gordon Moore back in the 1960s, which assumes that the number of transistors in an integrated circuit doubles roughly every 18 months — initially, 24 months — effectively increasing microchip complexity. The problem is, Moore’s Law is nearing its end, as transistors can no longer be effectively miniaturized to increase chip performance.

This predicament, coupled with the need for higher levels of processing power, presents a hurdle that must be overcome in the continued development of artificial intelligence (AI). The Defense Advanced Research Projects Agency (DARPA), the research arm of the U.S. Defense Department, thinks that developing specialized circuits — or application-specific integrated circuit chips (ASICs) — is one of the ways to overcome these limitations. On Wednesday last week, DARPA announced a couple of efforts working on this concept, as part of their Electronics Resurgence Initiative.

One project is the Software Defined Hardware, which is developing “a hardware/software system that allows data-intensive algorithms to run at near ASIC efficiency without the cost, development time or single application limitations associated with ASIC development.” The second project is called Domain-Specific System on a Chip. Simply put, this takes a combined approach of using general purpose chips, hardware coprocessors, and ASICs, “into easily programmed [systems on a chip] for applications within specific technology domains.”

AI’s Limits?

Experts generally agree that Moore’s Law will no longer be viable by the 2020s. Meanwhile, what AI is being trained to do requires a great deal more processing power — close to what the human brain is capable of. Increasing processing power, something quantum computing and IBM’s neurosynaptic chips promise, will be crucial to continuing the development of AI.

“If you’re willing to work on specialized classes of problems, you can actually get a lot more out of specialized architectures,” DARPA director Arati Prabhakar told Defense One back in 2014. “Special architectures will give us many more steps forward.”

Much has been said about AI’s potential for both good and bad. Tesla and OpenAI CEO Elon Musk has long-been one of the greatest advocates of regulating AI development, and he’s even warned about how the race for AI superiority could lead to a third world war. Yet all of the astonishing things AI seems capable of right now, and what it could still be made to do in the future, will not be possible if the microchip problem isn’t solved.

For DARPA, it’s a problem that needs a more immediate solution. Artificially intelligent systems are being trained to think like human beings do, or even better. Yet, the human brain’s ability to process information and data remains unparalleled, even by today’s best artificial neural networks — which depend on the computing power of microchips. Or perhaps Musk is also right in thinking that one other solution is to combine the human mind with machines.

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